Agile data science / Russell Jurney.

Format:

Book

Author/Creator:

Jurney, Russell.

Contributor:

Loukides, Michael Kosta, editor.

Treseler, Mary, editor.

Language:

English

Subjects (All):

Apache Hadoop.

Data mining.

Agile software development.

Physical Description:

1 online resource (177 p.)

Edition:

First edition.

Place of Publication:

Beijing : O'Reilly Media, 2013.

Language Note:

English

System Details:

text file

Summary:

Mining big data requires a deep investment in people and time. How can you be sure you’re building the right models? With this hands-on book, you’ll learn a flexible toolset and methodology for building effective analytics applications with Hadoop. Using lightweight tools such as Python, Apache Pig, and the D3.js library, your team will create an agile environment for exploring data, starting with an example application to mine your own email inboxes. You’ll learn an iterative approach that enables you to quickly change the kind of analysis you’re doing, depending on what the data is telling you. All example code in this book is available as working Heroku apps. Create analytics applications by using the agile big data development methodology Build value from your data in a series of agile sprints, using the data-value stack Gain insight by using several data structures to extract multiple features from a single dataset Visualize data with charts, and expose different aspects through interactive reports Use historical data to predict the future, and translate predictions into action Get feedback from users after each sprint to keep your project on track

Contents:

Intro

Copyright

Table of Contents

Preface

Who This Book Is For

How This Book Is Organized

Conventions Used in This Book

Using Code Examples

Safari® Books Online

How to Contact Us

Part I. Setup

Chapter 1. Theory

Agile Big Data

Big Words Defined

Agile Big Data Teams

Recognizing the Opportunity and Problem

Adapting to Change

Agile Big Data Process

Code Review and Pair Programming

Agile Environments: Engineering Productivity

Collaboration Space

Private Space

Personal Space

Realizing Ideas with Large-Format Printing

Chapter 2. Data

Email

Working with Raw Data

Raw Email

Structured Versus Semistructured Data

SQL

NoSQL

Serialization

Extracting and Exposing Features in Evolving Schemas

Data Pipelines

Data Perspectives

Networks

Time Series

Natural Language

Probability

Conclusion

Chapter 3. Agile Tools

Scalability = Simplicity

Agile Big Data Processing

Setting Up a Virtual Environment for Python

Serializing Events with Avro

Avro for Python

Collecting Data

Data Processing with Pig

Installing Pig

Publishing Data with MongoDB

Installing MongoDB

Installing MongoDB's Java Driver

Installing mongo-hadoop

Pushing Data to MongoDB from Pig

Searching Data with ElasticSearch

Installation

ElasticSearch and Pig with Wonderdog

Reflecting on our Workflow

Lightweight Web Applications

Python and Flask

Presenting Our Data

Installing Bootstrap

Booting Boostrap

Visualizing Data with D3.js and nvd3.js

Chapter 4. To the Cloud!

Introduction

GitHub

dotCloud

Echo on dotCloud

Python Workers

Amazon Web Services

Simple Storage Service

Elastic MapReduce

MongoDB as a Service

Instrumentation

Google Analytics

Mortar Data.

Part II. Climbing the Pyramid

Chapter 5. Collecting and Displaying Records

Putting It All Together

Collect and Serialize Our Inbox

Process and Publish Our Emails

Presenting Emails in a Browser

Serving Emails with Flask and pymongo

Rendering HTML5 with Jinja2

Agile Checkpoint

Listing Emails

Listing Emails with MongoDB

Anatomy of a Presentation

Searching Our Email

Indexing Our Email with Pig, ElasticSearch, and Wonderdog

Searching Our Email on the Web

Chapter 6. Visualizing Data with Charts

Good Charts

Extracting Entities: Email Addresses

Extracting Emails

Visualizing Time

Chapter 7. Exploring Data with Reports

Building Reports with Multiple Charts

Linking Records

Extracting Keywords from Emails with TF-IDF

Chapter 8. Making Predictions

Predicting Response Rates to Emails

Personalization

Chapter 9. Driving Actions

Properties of Successful Emails

Better Predictions with Naive Bayes

P(Reply | From & To)

P(Reply | Token)

Making Predictions in Real Time

Logging Events

Index

About the Author.

Notes:

Includes index.

Description based on online resource; title from PDF title page (ebrary, viewed November 13, 2013).

ISBN:

9781449326906

1449326900

9781449326920

1449326927

9781449326913

1449326919

9781306813136

1306813131

OCLC:

868971089